Impaired littoral energy pathways cause a shift to pelagic resources by fish in recovering lake food webs
Bibliographic record
Abstract
Abstract Fish communities may increase in biomass and productivity due to energy subsidies from the littoral invertebrate community. In lakes recovering from acidification and metal contamination, such as those in Sudbury, Ontario, Canada, impaired benthic invertebrate communities (i.e., low diversity with higher abundance of small‐bodied taxa) allowed a critical test of the role of these littoral pathways on fish diet. We compared fish abundance, diversity, diet and biomass in eight recovering and eight reference lakes and related availability of the main littoral and pelagic invertebrate groups to fish diet regime using stable isotope analysis. A Bayesian mixing model (MixSIR) was used to estimate diet likelihood, and convex hull analysis was used to estimate trophic niche space of fish communities. Fish biomass did not differ between impaired and reference lakes despite substantial differences in potential diet. Fish depended strongly on littoral benthos in the reference lakes but consumed more pelagic food in the impaired lakes. The trophic niche of the focal, most common fish species (i.e., yellow perch, smallmouth bass, pumpkinseed and brown bullhead) was larger in the impaired lakes. We attributed these differences to low diversity at the highest trophic levels of fish communities in the impaired lakes as well as to depauperate benthic invertebrate communities. In contrast to the food webs of most temperate lakes, fish in impaired lakes preyed less on littoral invertebrates yet still managed to maintain a reference lake level of biomass standing crop by relying more on pelagic resources – macro zooplankton such as Chaoborus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".